BUSINESS QUESTION: Which are the top 5 products that are going to be more profitable for the company?

What data do we have?

New product attributes and existing product attributes.

• Predicting sales of four different product types: PC, Laptops, Netbooks and Smartphones
• Assessing the impact services reviews and customer reviews have on sales of different product types

1. Data cleaning

2. Data exploration

3. Pre-process: feature selection (correlation matrix) & feature engineering

4. Modalization: linear regresion, KNN, SVM, Random forest, GBM

5. Error analysis

Transformation to factor:

fact_var <- c("ProductType","ProductNum")
ex_prod[,fact_var] <- apply(ex_prod[,fact_var], 2, as.factor)

Giving names to the rows:

ex_prod <- tibble::column_to_rownames(.data = ex_prod,
                                     var = "ProductNum")
ex_prod$ProductNum <- NULL

# train and test
train_id <- createDataPartition(y = ex_prod$Volume, p = 0.80, list = F)
train <- ex_prod[train_id,]
test <- ex_prod[-train_id,]

# create linear regression model
mod_lm <- lm(formula = Volume ~ ., data = train)

# model performance
postResample(pred = predict(object = mod_lm, newdata = test),
             obs = test$Volume)

##         RMSE     Rsquared          MAE 
## 6.413444e-14 1.000000e+00 3.896143e-14

Main predictors:

1. 5 stars
2. Product type: Game console

##        RMSE    Rsquared         MAE 
## 120.0594381   0.9787394  83.0056191

ex_prod_4mod <- ex_prod_dummy %>% 
  dplyr::select(Pos_Ser, Neg_Ser, Recomend, PC, Laptop, 
                Netb, Smart_Ph, total_stars, Vol)

# creating trainin and testing with the features selected
set.seed(123)
train_id <- createDataPartition(y = ex_prod_4mod$Vol,
                                p = 0.80,
                                list = F)
train <- ex_prod_4mod[train_id,]
test <- ex_prod_4mod[-train_id,]

# model creation
mod_4lm <- lm(formula = Vol ~., data = train)

# metrics
postResample(pred = predict(object = mod_4lm, newdata = test),
             obs = test$Vol)

##        RMSE    Rsquared         MAE 
## 151.3046880   0.8931816 100.0846194

The model has decrease the performance. Let's see how is performing to the categories we are interested.

# modalization with knn
mod_5knn <- knn.reg(train[,sc_var], # predictores que usaremos para predecir 
                    test[,sc_var], # validadores, los predictores que queremos usar para validar
                    train$Vol, # salida que tiene que dar en el conjunto de entrenamiento
                    k = 1, # número de vecinos
                    algorithm = "brute") # algoritmos que se pueden usar

# checking the metrics 
postResample(pred = mod_5knn$pred, obs = test$Vol)

##        RMSE    Rsquared         MAE 
## 218.9094181   0.7863258 123.6666667